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dc.contributor.authorHan, Shih-Pingen_US
dc.date.accessioned2007-04-23T18:20:25Z
dc.date.available2007-04-23T18:20:25Z
dc.date.issued1977-09en_US
dc.identifier.citationhttp://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR77-322en_US
dc.identifier.urihttps://hdl.handle.net/1813/7443
dc.description.abstractWe develop a class of methods for minimizing a nondifferentiable function which is the maximum of a finite number of smooth functions. The methods proceed by solving iteratively qquadratic programming problems to generate search directions. For efficiency the matrices in the quadratic programming problems are suggested to be updated in a variable metric way. By doing so, the methods possess many attractive features of variable metric methods and can be viewed as their natural extension to the nondifferentiable case. To avoid the difficulties of an exact line search, a practical stepsize procedure is also introduced. Under mild asumptions the resulting method converge globally.en_US
dc.format.extent732007 bytes
dc.format.extent220238 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.language.isoen_USen_US
dc.publisherCornell Universityen_US
dc.subjectcomputer scienceen_US
dc.subjecttechnical reporten_US
dc.titleVariable Metric Methods for Minimizing a Class of Nondifferentiable Functionsen_US
dc.typetechnical reporten_US


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